




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def    snake_case(							__magic_name__					,						__magic_name__       )					->				float:





     '''simple docstring'''


     return math.sqrt(sum(pow(a - b					,						2       ) for a, b in zip(_A					,						_A       )       )       )






def    snake_case(							__magic_name__					,						__magic_name__       )					->				list[list[list[float] | float]]:





     '''simple docstring'''


     if dataset.ndim != value_array.ndim:
          lowercase   :  Optional[Any]        =					(
              '''Wrong input data\'s dimensions... '''
              F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
          )
          raise ValueError(_A       )

     try:
          if dataset.shape[1] != value_array.shape[1]:
               lowercase   :  List[Any]        =					(
                   '''Wrong input data\'s shape... '''
                   F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
               )
               raise ValueError(_A       )
     except IndexError:
          if dataset.ndim != value_array.ndim:
               raise TypeError('''Wrong shape'''       )

     if dataset.dtype != value_array.dtype:
          lowercase   :  Optional[int]        =					(
              '''Input data have different datatype... '''
              F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
          )
          raise TypeError(_A       )

     lowercase   :  Tuple        =					[]

     for value in value_array:
          lowercase   :  Dict        =					euclidean(_A					,						dataset[0]       )
          lowercase   :  Dict        =					dataset[0].tolist()

          for dataset_value in dataset[1:]:
               lowercase   :  Union[str, Any]        =					euclidean(_A					,						_A       )

               if dist > temp_dist:
                    lowercase   :  Any        =					temp_dist
                    lowercase   :  List[str]        =					dataset_value.tolist()

          answer.append([vector, dist]       )

     return answer






def    snake_case(							__magic_name__					,						__magic_name__       )					->				float:





     '''simple docstring'''


     return np.dot(_A					,						_A       ) / (norm(_A       ) * norm(_A       ))


if __name__ == "__main__":
 import doctest

 doctest.testmod()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


